Focused shape restoration realizes 3D shape reconstruction by modeling the potential relationship between scene depth and defocus blur. However， the existing 3D shape reconstruction network cannot effectively utilize the sequential correlation of image sequences for representation learning. Therefore， a depth network framework based on spatial correlation features of multi-depth image sequences， namely 3D Spatial Correlation Horizon Analysis Model （3D SCHAM）， was proposed for 3D shape reconstruction， by which not only the edge features could be accurately captured from the focus region to the defocus region in a single image frame， but also the spatial dependence features between different image frames could be utilized effectively. Firstly， the temporal continuous model for 3D shape reconstruction was constructed by constructing a network with composite extension of depth， width and receptive field to determine the single point depth results. Secondly， an attention module based on spatial correlation was introduced to fully learn the spatial dependence relationships of “adjacency” and “distance” between frames. In addition， residual-reversal bottleneck was used for resampling to maintain semantic richness across scales. Experimental results on DDFF 12-Scene real scene dataset show that compared with DfFintheWild model， the accuracy of 3D SCHAM model at three thresholds is improved by 15.34%， 3.62% and 0.86% respectively， verifying the robustness of 3D SCHAM in real scenes.
